Optimized recovery method of wave field characteristics of tunnel drilling source of drilling jumbo and its engineering application

Ronghui Bai , Shaoshuai Shi , Weidong Guo , Zhenyan Tian

Urban Lifeline ›› 2025, Vol. 3 ›› Issue (1)

PDF
Urban Lifeline ›› 2025, Vol. 3 ›› Issue (1) DOI: 10.1007/s44285-025-00041-8
Research
research-article

Optimized recovery method of wave field characteristics of tunnel drilling source of drilling jumbo and its engineering application

Author information +
History +
PDF

Abstract

This study addresses the significant issue of wavefield mixing in seismic data acquired from tunnel drilling jumbo during urban tunnel construction. This paper compares and analyzes the effect of time-domain cross-correlation in the wavelet transform domain through simulation, and concludes that the combination of the time–frequency cross-correlation in the wavelet transform domain, which excels in noise suppression and in the ability of extracting related information, and the algorithm of spike deconvolution. An optimized recovery method for the wavefield characteristics of drilling jumbo drilling source is implemented. This paper focuses on the single-arm drilling source signal of drilling jumbo as the research object, and the equivalent pulse signal of the drilling source is obtained through the spike deconvolution. The equivalent pulse signal is then optimized using time–frequency cross-correlation in the wavelet transform domain, which improves the ability of extracting the valid reflective information and enhances the effect of the recovery of seismic data of drilling source of the drilling jumbo. The recovery effectiveness of the method of spike deconvolution-wavelet domain cross-correlation on the seismic recordings of the drilling source is analyzed, and the method is applied to practical engineering scenarios, thereby validating the effectiveness and feasibility of the method.

Keywords

Drilling jumbo / Spike deconvolution / Cross-correlation / Seismic data recovery / Advanced geological prediction

Cite this article

Download citation ▾
Ronghui Bai, Shaoshuai Shi, Weidong Guo, Zhenyan Tian. Optimized recovery method of wave field characteristics of tunnel drilling source of drilling jumbo and its engineering application. Urban Lifeline, 2025, 3(1): DOI:10.1007/s44285-025-00041-8

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

DuYB, JiangLW, ChenMH, WangZW. Development and prospect of geological surveying technology for railway tunnels in China[J]. Tunnel Construction, 2021, 41111943.

[2]

GuoXH. Analysis of geological investigation technologies for underwater highway tunnels[J]. Tunnel Construction, 2016, 36101190.

[3]

HeJH, LiDQ, HuYF, LingF, WangJH. Geophysical exploration methods for strong interference urban underground space[J]. Chinese Journal of Engineering Geophysics, 2022, 195559.

[4]

Zhao P, Jiang J, Wang XR (2017) Urban underground space exploration key technologies and development trend[J]. Coal Geology of China 29(9):61. https://doi.org/10.3969/j.issn.1674–1803.2017.09.12

[5]

ZhaoY, ShiSS, TianSM, LiGL, TaoWM, GuoWD. Technical Difficulties and Countermeasure Suggestions in Tunnel Construction of Ya'an-Linzhi Section of Sichuan-Tibet Railway[J]. Tunnel Construction, 2021, 41(07): 1079-1090.

[6]

LiSC, LiuB, SunHF, NieLC, ZhongSH, SuMX, LiX, XuZH. State of art and trends of advanced geological prediction in tunnel construction. J Chin J Rock Mech Eng., 2014, 33(06): 1090-1113.

[7]

Cui P, Ge YG, Li SJ, et al. (2022) Scientific challenges in disaster risk reduction for the Sichuan-Tibet Railway[J]. Engineering Geology, 309. https://doi.org/10.1016/j.enggeo.2022.106837 

[8]

LiSC, LiuB, XuXJ, NieLC, LiuZY, SongJ, SunHF, ChenL, FanKR. An overview of ahead geological prospecting in tunneling[J]. Tunnelling And Underground Space Technology, 2017, 63: 69-94.

[9]

TianSM, WangW, LiGL, TaoWM, ShiSS. Design concept and main principles of tunnel on Sichuan-Tibet railway [J]. Tunnel Construction, 2021, 41(4): 519-530.

[10]

Staron P, Arens G, Gros P (1988−01−05) Method of instantaneous acoustic logging within a wellbore: U.S, [P]. 4718048

[11]

Poletto,Flavio; Miranda,Francesco (2004) Seismic while drilling. Elsevier Science Ltd, Amsterdam

[12]

Rector J, Marion B, Widrow B, et al. (1993−03−02) Signal processing to enable utilization of a rig reference sensor with a drill bit seismic source: US, [P]. US5050130 A

[13]

ZhangSH, HanJY, ZhuGF. Theory and Engineering Application of Seismic While Drilling [J]. ACT A PETROLEI SINICA, 1999, 20(2): 67-72

[14]

HanJY. Prediction of Formation Parameters in Seismic While Drilling[J]. Well Logging Technology, 2000, 24(3): 176-178.

[15]

Luo B (2005) Seismic while drilling (SWD) technique research and its application effect. Dissertation, China University of Geosciences (Beijing)

[16]

PolettoF, MirandaF, FarinaB. Seismic-while-drilling drill-bit source by ground force: Concept and application[J]. Geophysics, 2020, 85(3): MR167-MR178.

[17]

JinZD, NengCX, LiuYQ, DongL. SWD signal processing based on wavelet domain correlation algorithm [J]. J China Coal Soc, 2012, 37(04): 621-626.

[18]

PeacockKL, TreitelS. Predictive Deconvolution-Theory and Practice[J]. Geophysics, 1969, 342155.

[19]

Wang L F (2009) Study of the key technologies of seismic exploration while drilling. Dissertation, Ocean University of China

[20]

Sun BC, A Bóna, Zhou BZ, King A, Dupuis C, Kepic A (2015) Drill-rig noise suppression using the Karhunen-Loéve transform for seismic-while-drilling experiment at Brukunga, South Australia[J]. Explor Geophys 47. https://doi.org/10.1071/EG14086

[21]

Gao W (2011) Blind deconvolution based on independent component analysis. Dissertation, Ocean University of China

[22]

QinXK, WangLF, ZhangBL. Denoising with sparse decomposition for the reference signal of seismic while drilling[J]. China Sciencepaper, 2016, 11(21): 2473-2478

[23]

XuLW, ChenH, ZhangXM, WangXM. Green's function retrieval with Marchenko and inter-source seismic interferometry method for drill-bit seismic while drilling[J]. J Geophys Eng, 2018, 15(5): 2047-2059.

[24]

RectorJWIII. Drill string wave modes produced by a working drill bit[J]. Seg Technical Program Expanded Abstracts, 1992, 1111410.

[25]

PolettoF, CarcioneJM, LovoM, MirandaF. Acoustic velocity of seismic-while-drilling (SWD) borehole guided waves[J]. Geophysics, 2002, 67(3): 921-927.

[26]

Sun HZ (2021) Tunnel drill jambo active source seismic wave field characteristics and effective wave identification method and application. Dissertation, Shandong University

[27]

MahdadA, DoulgerisP, BlacquiereG. Separation of blended data by iterative estimation and subtraction of interference noise[J]. Geophysics, 2011, 7634453.

[28]

Gao JH, Mao J, Man WS, Chen WC, Zheng QQ (2006) On the denoising method of prestack seismic data in wavelet domain[J]. Chin J Geophys-Chin Ed 49(4):1155–1163

[29]

NeelamaniR, BaumsteinAI, GillardDG, HadidiMT, SorokaWL. Coherent and random noise attenuation using the curvelet transform[J]. Lead Edge, 2012, 27(2): 240-248.

[30]

ShiSS, CaoTY, XuXJ, et al.. Three-arm Mixed Pilot Signal Blind Source Separation Method and Engineering Application of Rock Breaking Seismic Source of Tunnel Rock Drilling Rig [J]. J Basic Sci Eng, 2021, 29(05): 1124-1139.

[31]

Guo WD (2023) Research on Recovery and gain method of reflection wave field charactreistics of tunnel drilling jumbo while drilling. Dissertation. Shandong University. https://doi.org/10.27272/d.cnki.gshdu.2023.007283

[32]

YangL, ChenW, WangH, et al.. Deep Learning Seismic Random Noise Attenuation via Improved Residual Convolutional Neural Network[J]. IEEE Trans Geosci Remote Sens, 2021, 59(9): 7968-7981.

[33]

PeregD, CohenI, VassiloiouAA. Sparse seismic deconvolution via recurrent neural network[J]. J Appl Geophys, 2020, 175103979

[34]

ChaiXT, TangGY, LinK, et al.. Deep learning for multitrace sparse-spike deconvolution[J]. Geophysics, 2021, 86(3): V207-V218.

[35]

Phan S, Sen M K (2021) Seismic nonstationary deconvolution with physics-guided autoencoder[C]//SEG/AAPG/SEPM First Inter-national Meeting for Applied Geoscience & Energy. OnePetro. https://doi.org/10.1190/segam2021-3582130.1

[36]

GaoZQ, HuSC, LiC, et al.. A deep-learning-based generalized convolutional model for seismic data and its application in seismic deconvolution[J]. IEEE Trans Geosci Remote Sens, 2022, 60: 1-17

[37]

HolbergO. Computational aspects of the choice of operator and sampling interval for numerical differentiation in large-scale simulation of wave phenomena[J]. Geophys Prospect, 1987, 35(6): 629-655.

[38]

DaiRH, YinC, YangSS, ZhangFC. Seismic deconvolution and inversion with erratic data[J]. Geophys Prospect, 2018, 66(9): 1684-1701.

[39]

Zhao WN (2013) Reference signal anaysis of seismic while drilling. Dissertation, Ocean University of China

[40]

ShiSS, SunHZ, LiSC, XuXJ, ZhouZQ, GuoWD, CaoTY. Reconstruction method of seismic record of drilling jumbo rock-breaking seismic source[J]. Journal of Central South University (Science and Technology), 2021, 52(12): 4405-4414.

[41]

Kazemi N, Shor R, Innanen K A (2018) Imaging with a seismic-while-drilling dataset[C]. CSPG CSEG CWLS Convention

[42]

Luo GK (2007) A study on morlet wavelet transform theory and application with software implementation. Dissertation. Nanjing University of Aeronautics and Astronautics

[43]

LiQ, Li HuaHuWY, et al.. Transparent Operator Network: A Fully Interpretable Network Incorporating Learnable Wavelet Operator for Intelligent Fault Diagnosis[J]. IEEE Trans Industr Inf, 2024, 20: 8628-8638.

[44]

ZhangHJ, ChenZB, LiH, YangXL. Seismic Wavefield Simulation of Tunnel Advance Detection Based on High-Order Staggered-Grid Finite Difference Method[J]. Tunnel Construction, 2021, 41(7): 1172-1179.

Funding

National Natural Science Foundation of China(Grant No.52278404)

Taishan Scholar Foundation of Shandong Province(Grant No.tsqn202103002)

Future Scholars project of Shandong University

RIGHTS & PERMISSIONS

The Author(s)

AI Summary AI Mindmap
PDF

113

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/